The DOI matches its Crossref record; 205 of the 207 references it cites were found in a registry or an index.
A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics
Open accessAbstract
The utilization of large language models (LLMs) for Healthcare has generated both excitement and concern due to their ability to effectively respond to free-text queries with certain professional knowledge. This survey outlines the capabilities of the currently developed Healthcare LLMs and explicates their development process , to provide an overview of the development road map from traditional Pretrained Language Models (PLMs) to LLMs. Specifically, we first explore the potential of LLMs to enhance the efficiency and effectiveness of various Healthcare applications highlighting both the strengths and limitations. Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, and summarize related Healthcare training data , learning methods, and usage. Finally, the unique concerns associated with deploying LLMs are investigated, particularly regarding fairness, accountability, transparency, and ethics. Besides, we support researchers by compiling a collection of open-source resources 1 . Summarily, we contend that a significant paradigm shift is underway, transitioning from PLMs to LLMs. This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a move from model-centered methodologies to data-centered methodologies. We determine that the biggest obstacle of using LLMs in Healthcare are fairness, accountability, transparency and ethics.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Privacy concerns are exacerbated in regions where cloud-based access is needed, or where external storage is required, and where data privacy regulations do not exist [51].”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [51]
In the citing paper
“Physicians are now expected not only to interpret biological signals and make clinical decisions but also to understand how algorithmic systems process data, the biases they may inherit, and how these biases influence patient care [3, 4].”
From Institutionalizing convergence education for medical artificial intelligence (2025) · cites this paper as [4]
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 13,453
DrugBank 5.0: a major update to the DrugBank database for 2018
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083
Research works printed but not linked: 9
- Jacob Devlin Ming-Wei Chang Kenton, Lee Kristina Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in: Proceedings of NAACL-HLT, 2019, pp. 4171–4186. · DOI · Paper page
- David Milward, Martin Beveridge, Ontology-based dialogue systems, in: Proc. 3rd Workshop on Knowledge and Reasoning in Practical Dialogue Systems, IJCAI03, 2003, pp. 9–18.
- Wissam Antoun, et al., AraBERT: Transformer-based Model for Arabic Language Understanding, in: Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, 2020, pp. 9–15.
- Kawazoe, 2020, A clinical specific BERT developed with huge size of Japanese clinical narrative, MedRxiv
- Suhyeon Lee, et al., LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation, in: The Twelfth International Conference on Learning Representations, 2023.
- Gilson, 2023, How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment, JMIR Med. Educ., 9
- Yoshua Bengio, et al., From system 1 deep learning to system 2 deep learning, in: Neural Information Processing Systems, 2019.
- Byron C. Wallace, et al., Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization, in: Proceedings of AMIA Informatics Summit, 2021.
- Gao, 2024, Accurate spatial quantification in computational pathology with multiple instance learning, MedRxiv
Cited by
Sorted by citations
Large Language Models in Healthcare and Medical Applications: A Review
Maity, S., & Saikia, M. J. (2025) · Bioengineering · doi:10.3390/bioengineering12060631 · Cited by 249
Large language models for disease diagnosis: a scoping review
Zhou, S., Xu, Z., Zhang, M., Xu, C., Guo, Y., Zhan, Z., Fang, Y., Ding, S., Wang, J., Xu, K., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00011-z · Cited by 188
Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions
He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3496744 · Cited by 162
Zhu, X., Wang, Y., Cambria, E., Rida, I., López, J. S., Cui, L., & Wang, R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103268 · Cited by 130
Self-citationApplication of large language models in medicine
Liu, F., Zhou, H., Gu, B., Zou, X., Huang, J., Wu, J., Li, Y., Chen, S. S., Hua, Y., Zhou, P., et al. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00279-5 · Cited by 94
Metadata
- Authors
- He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E.
- Affiliations
- National University of Singapore (SG)
- Nanyang Technological University (SG)
- Corresponding author
- Feng, Mengling
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 118
- Pages
- 102963
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4406602253
- Cited by
- 147
- References
- 207
- Citations by year
- 2026: 59 · 2025: 83
- FWCI
- 34.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Fusion (publishedVersion, cc-by)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- National Research Foundation (AISG-GC-2019-001-2A, AISG2-TC-2022-004)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- large language models, medical applications, pre-trained language models, generative AI, fairness, accountability, transparency, AI ethics, open source resources
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
